The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications. Although many researches have been proposed to address this issue, they controlled the data missing conditions by simulation with self-defined masking lengths or sizes. Besides, the masking definitions are different among all these experimental settings. This paper presents a novel intermittent speech recovery (ISR) system for real-world self-powered intermittent devices. Three contributive stages: interpolation, enhancement, and combination are applied to the ISR system for speech reconstruction. The experimental results show that our recovery system increases speech quality by up to 591.7%, while increasing speech intelligibility by up to 80.5%. Most importantly, the proposed ISR system improves the WER scores by up to 52.6%. The promising results not only confirm the effectiveness of the reconstruction but also encourage the utilization of these battery-free wearable/IoT devices.

翻译：语音输入的不完整严重地降低了所有相关语音信号处理应用程序的性能。 虽然已提出许多研究来解决这一问题,但它们通过模拟以自定义的遮罩长度或大小控制数据缺失条件。 此外,所有这些实验环境的遮罩定义各不相同。本文为现实世界的自动间歇装置提供了一个新型的间歇语音恢复系统。三个辅助阶段:内插、增强和组合应用到IRS的语音重建系统。实验结果显示,我们的恢复系统将语音质量提高到591.7 %, 并将语言智能提高到80.5% 。最重要的是,拟议的IRS系统将WER的评分提高到52.6% 。有希望的结果不仅证实了重建的效果,而且还鼓励使用这些无电池耗损/IoT的装置。